You may have heard the term “Internet of Things.” This refers to the fact that many devices are now connected to the Internet, from your phone to your car to wearables like the Apple Watch and FitBit. It is estimated that by 2020, there will be 25 billion connected devices. These devices capture real time data, and allow for real-time alerts. They produce tons of data on the individual. In combination, they can provide us even more information on entire populations.

Big Data can fill in the blanks for predictive analytics, “the use of data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data.” Electronic Medical Records can be reviewed and analyzed. An individual patient generates much data which can be analyzed to make predictions on whether or not they will comply with their doctor’s recommendations. For example, one hospital found that patients who live in certain neighborhoods are likely to miss appointments. They concluded that it was actually cheaper to send them a taxi to bring them to the appointment than it was to deal with a missed appointment. This was determined by utilizing multiple data sources: patient data, neighborhood data, and administrative data.

Remember, these data are not all being collected by the researcher. They are being collected independently, and the researcher is able to query the different sources to make a prediction.

Prescriptive analytics are a goal of Big Data in healthcare–to be able to identify and predict the path of a patient, then intervene to set them on the right path. For example, if a patient is supposed to walk a certain number of minutes a day, their phone or wearable would be able to see, in real time, if they choose to do so. If the patient allows these data to be shared with their physician, the physician can connect with the patient and determine why they are not complying. This would allow for immediate interventions that were not possible before.

When a person uses their cell phone late at night, it may indicate they are having trouble sleeping, which their physician can then address. These are very simple examples, but they demonstrate how real-time data can be captured and used to nudge patients in the proper direction.

Genomics research is a third area of opportunity in Big Data. The cost of mapping out an individual’s genome has plummeted since the completion of the human genome project. The individual’s genome itself is a massive dataset. When you can compare the genomes of millions of people, you can gain insight into the effectiveness of medicines. We are already seeing a move towards personalized medicine, which will only be strengthened by the Big Data revolution.

Traditionally, an oncologist might find that patients of European descent respond differently from non-Europeans to a particular treatment, which can then be used to determine the first-line or second-line treatment for those subpopulations. Now, with genomic testing, oncologists can see that those with a particular genetic marker respond very well or not at all to a particular treatment. With rapid genomic testing, the oncologist can then use a patient’s genomic information to recommend the treatment most likely to be effective. We are now able to identify patient sub-populations based on genetic markers, which allows for targeted gene therapy, Think of the advances this will bring us in treating cancer or other devastating diseases.

As more genomic data are captured and compared, we will be able to make insights that were nearly impossible to make before. We can begin to see what was once invisible. The more data there are, the more insights we can glean.

When enough Big Data are available, the insights we will be able to make are beyond comprehension. It is already transforming how we think of health and public health and it will continue to revolutionize healthcare for years to come.